NaiveBayes#
- class capymoa.classifier.NaiveBayes[source]#
Bases:
MOAClassifier
Naive Bayes incremental learner. Performs classic Bayesian prediction while making the naive assumption that all inputs are independent. Naive Bayes is a classifier algorithm known for its simplicity and low computational cost. Given n different classes, the trained Naive Bayes classifier predicts, for every unlabeled instance I, the class C to which it belongs with high accuracy.
- Parameters:
schema – The schema of the stream, defaults to None.
random_seed – The random seed passed to the MOA learner, defaults to 0.
- predict(instance)[source]#
Predict the label of an instance.
The base implementation calls
predict_proba()
and returns the label with the highest probability.- Parameters:
instance – The instance to predict the label for.
- Returns:
The predicted label or
None
if the classifier is unable to make a prediction.
- predict_proba(instance)[source]#
Return probability estimates for each label.
- Parameters:
instance – The instance to estimate the probabilities for.
- Returns:
An array of probabilities for each label or
None
if the classifier is unable to make a prediction.
- train(instance)[source]#
Train the classifier with a labeled instance.
- Parameters:
instance – The labeled instance to train the classifier with.
- random_seed: int#
The random seed for reproducibility.
When implementing a classifier ensure random number generators are seeded.